Aim 1. We demonstrated previously that sleep can vary among flies having identical genotypes subjected to identical environmental conditions. We quantified this variability as the sensitivity to the environment. Sensitivity to the environment is strongly correlated with sleep duration; flies having short night sleep were far more sensitive to random environmental perturbations than longer-sleeping flies. As this sensitivity is heritable, we identified many polymorphisms putatively involved in its regulation. How these polymorphisms cause variable sleep phenotypes among flies having the same genotype and subjected to the same environmental conditions is not known, but one distinct possibility is that environmental sensitivity might alter gene expression. Therefore, our first goal is to determine whether transcripts are altered among flies with identical genotypes subjected to a common environment. If significant changes in gene expression among identical individuals exist, then it would indicate a possible mechanism by which different sleep phenotypes might arise in individuals of the same genotype. We examined the changes in endogenous gene expression in a sample of 16 lines randomly chosen from the Drosophila Genetic Reference Panel (DGRP). Each line was subjected to the standard environment of sucrose-cornmeal food, 25 deg. C, 60% humidity, and 12:12 hour light:dark cycle. Three biological replicates of the same environment were made. Individual virgin males and females were harvested, and total RNA was extracted. We constructed RNA sequence libraries and successfully sequenced RNA from 750 of the 768 individual flies that were frozen; 726 (730 mapped to the 6.0 Drosophila genome) samples passed genotype and sex quality control guidelines. Aim 2. As there are no clear-cut strategies for the normalization and analysis of RNA-Seq data, we compared eight different popular normalization methods for raw count data, three data filtering methods, and two statistical analysis approaches. We found that one statistical analysis approach using either of two normalization methods was the most appropriate strategy for identifying differentially expressed genes. We summarized our findings in a manuscript entitled, Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual Drosophila melanogaster; this work was published in BMC Genomics in 2016. In addition, the raw sequence data and details of the experiment have been deposited in the Gene Expression Omnibus, accession number GSE60314, mRNA sequence data of individual Drosophila melanogaster male and female flies from 16 Drosophila Genetic Reference Panel lines reared in replicated environments. Aim 3. Using our preferred analysis methodology, we have identified genes that are significantly differentially expressed across environment, genotype, sex, and their interactions. We have identified genes with differential expression among identical genotypes reared in the same environment. Furthermore, we identified genes having a genetic component of micro-environmental plasticity. We have summarized these findings in a second manuscript entitled Micro-environmental gene expression plasticity among individual Drosophila melanogaster which was published in G3 in 2016. Aim 4. Along with our collaborators (Teresa Przytycka, Brian Oliver, and Steven Russell), we analyzed the large data set of 768 RNA sequences to validate the coefficient of variation of gene expression as a measure of cell-cell expression noise. Our analysis became part of the publication Dosage Dependent Expression Variation Suppressed on the Drosophila Male X Chromosome, which was published in G3 in 2018. Aim 5. Day-to-day variability in sleep has been associated with mental health disorders in humans. We demonstrated that this day-to-day variability is due in part to genotype through our analysis of previously collected phenotypic data from the Drosophila Genetic Reference Panel. We found that flies have very high levels of day-to-day or intra-individual variability in all sleep traits tested. We conducted a genome-wide association study using the standard deviation of each sleep trait across days as a quantitative trait. We found 104 candidate genes for intra-individual variability, and some of these genes have human homologs. We tested mutations in 14 candidate genes and verified the effects on intra-individual variability for 12 of these genes. We published the results in Genotype influences day-to-day variability in sleep in Drosophila melanogaster in Sleep in 2017.